most citedSemi-supervised Learning with Sparse Autoencoders in Phone Classification

4 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

eess.AS20245 cited

Developing Acoustic Models for Automatic Speech Recognition in Swedish

Giampiero Salvi

This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done empl…

eess.AS202412 cited

Dynamic Behaviour of Connectionist Speech Recognition with Strong Latency Constraints

Giampiero Salvi

This paper describes the use of connectionist techniques in phonetic speech recognition with strong latency constraints. The constraints are imposed by the task of deriving the lip…

eess.AS20248 cited

Segment Boundary Detection via Class Entropy Measurements in Connectionist Phoneme Recognition

Giampiero Salvi

This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rati…

cs.CV20221 cited

Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation

Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi +1

We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, e…

stat.ML20164 cited

Semi-supervised Learning with Sparse Autoencoders in Phone Classification

Akash Kumar Dhaka, Giampiero Salvi

We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. A…